NSF/SBE-BSF: Neural patterns underlying the development of planning in action production and anticipation in action perception

运动技能的一个显着方面是能够灵活、有目的地使用各种物体来计划行动。我们通过在考虑最终目标的情况下计划与物体的初始接触来实现这一点，即使最终目标需要多个步骤才能实现。因此，行动计划的过程涉及感知、认知和运动行为的整合。据推测，当我们预测他人的目标并观察他们的行为时，就会发生类似的整合过程。先前的研究表明，行动计划始于婴儿期，并随着年龄的增长而改善，但人们对这些与年龄相关的改善背后的伴随大脑活动知之甚少。这项跨学科研究结合了发展心理学、神经科学和计算机科学的概念、方法和分析技术，以了解儿童 1) 采取多个步骤实现目标的行动以及 2) 当他们观察其他人执行该行动时，行动计划中与年龄相关的变化。该研究采用了一种新颖的儿童记录方法组合：同时记录视频、眼动追踪、运动追踪和脑电图 (EEG)。这些方法和数据将通过数据库存储库中的研究视频和生理数据的开放共享来推动该领域的发展。算法和分析技术将在开放科学框架中共享。研究人员结合了行为测量、神经活动记录和机器学习技术，以了解儿童和成人如何 1）执行涉及最终目标预期的复杂运动任务，以及 2）被动观察其他人执行涉及预期最终目标的多步骤行动计划的任务。对动作表现的分析将侧重于计划不同阶段的行为的神经相关性，并将评估开始运动之前的神经活动是否可以预测幼儿计划能力的试验间行为变异性。行动观察分析将调查神经活动的年龄差异，同时观察其他人执行的行动是否显示出长期计划的证据。研究人员还将比较被动动作观察和动作表现期间预期的神经生理学特征。这些发现将有助于我们了解整个童年时期行动计划的发展。多模态记录方法和先进的多变量分析技术将为 STEM 和其他学科的研究铺平道路，探索儿童大脑和行为的发展变化。该奖项是 NSF/BSF 经济学和心理学合作机会的一部分。

A remarkable aspect of motor skill is the ability to plan actions flexibly and purposefully using a variety of objects. We achieve this by planning our initial contact with the object with the end goal in mind, even when the end goal requires multiple steps to be achieved. Thus, the process of action planning involves integration of perception, cognition, and motor behavior. Presumably, a similar integrative process occurs when we anticipate other people's goals while observing them perform actions. Previous work shows that action planning begins in infancy and improves with age, but little is known about the accompanying brain activity that underlies these age-related improvements. This interdisciplinary study combines concepts, methods, and analytic techniques from developmental psychology, neuroscience, and computer science to understand age-related changes in action planning while children 1) perform an action with multiple steps to the goal and 2) while they observe someone else perform the action. The research uses a novel combination of recording methods for children: Video, eye tracking, motion tracking, and electroencephalography (EEG) will be recorded simultaneously. The methods and data will advance the field through open sharing of the research videos and physiological data in the Databrary repository. Algorithms and analysis techniques will be shared in the Open Science Framework. The investigators combine behavioral measures, neural activity recordings, and machine-learning techniques in order to understand how children and adults 1) perform complex motor tasks that involve anticipation of the end-goal and 2) passively observe others performing tasks that involve multi-step action planning in anticipation of the end-goal. Analyses of action performance will focus on the neural correlates of behavior at different stages of planning and will assess whether neural activity prior to beginning a movement can predict trial-to-trial behavioral variability in young children's ability to plan. Analyses of action observation will investigate age differences in neural activity while observing others performing actions that do, or do not, show evidence of long-range planning. The investigators will also compare the neurophysiological signatures of anticipation during passive action observation and during action performance. These findings will inform our understanding of the development of action planning across childhood. The multi-modal recording methods and the advanced multivariate analytic techniques will pave the way for research in STEM and other disciplines to explore developmental changes in children's brain and behavior.This award is made as part of the NSF/BSF Opportunity for Collaborations in Economics and Psychology.